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240403 ||| eng |
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|a 9783031476068
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|a Di Ieva, Antonio
|e [editor]
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| 245 |
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|a The Fractal Geometry of the Brain
|h Elektronische Ressource
|c edited by Antonio Di Ieva
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| 250 |
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|a 2nd ed. 2024
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2024, 2024
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| 300 |
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|a XXIX, 1004 p. 285 illus., 211 illus. in color
|b online resource
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|a Fractal Dimension Studiesof the Brain Shape in Aging and Neurodegenerative Diseases -- Fractal Analysis in Neurodegenerative Diseases -- Fractal Analysis of the Cerebrovascular System Physiopathology -- Fractal and Chaos in the Hemodynamics of Intracranial Aneurysms -- Fractal-based Analysis of Arteriovenous Malformations (AVMs) -- Fractals in Neuroimaging -- Computational Fractal-Based Analysis of MR Susceptibility Weighted Imaging (SWI) in Neuro-oncology and neurotraumatology -- Texture Estimation for Abnormal Tissue Segmentation in Brain MRI -- Tumor Growth in the Brain: Complexity and Fractality -- Histological Fractal-based Classification of Brain Tumors -- Computational Fractal-based Analysis of the Brain Tumors Microvascular Networks -- Fractal analysis of electroencephalographic time-series (EEG-signals) -- On Multiscaling of Parkinsonian Rest Tremor Signals and Their Classification -- Fractals and Electromyograms -- Fractal analysis in Neuro-ophthalmology --
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|a Part I. Introduction to Fractal Geometry and its Applications to Neurosciences -- The Fractal Geometry of the Brain: An Overview -- 2. Box-Counting Fractal Analysis: A Primer for the Clinician -- Tenets and Methods of Fractal Analysis (1/f noise) -- 4. Tenets, Methods and Applications of Multifractal Analysis in Neurosciences -- Part II. Fractals in Neuroanatomy and Basic Neurosciences -- Fractals in Neuroanatomy and Basic Neurosciences: An Overview -- Morphology and Fractal-Based Classifications of Neurons and Microglia -- The Morphology of the Brain Neurons: Box-counting Method in Quantitative Analysis of 2D Image -- Neuronal Fractal Dynamics -- Does a Self-Similarity Logic Shape the Organization of the Nervous System? -- Fractality of Cranial Sutures -- The Fractal Geometry of the Human Brain: An Evolutionary Perspective -- Part III. Fractals in Clinical Neurosciences -- Fractal Analysis in Clinical Neurosciences: An Overview -- Fractal Analysis in Neurological Diseases --
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|a Fractals in Affective and Anxiety Disorders.-Fractal Fluency: An Intimate Relationship Between the Brain and Processing of Fractal Stimuli -- Part IV. Computational Fractal-Based Neurosciences -- Computational Fractal-based Neurosciences: An Overview -- ImageJ in Computational Fractal-based Neuroscience: Pattern Extraction and Translational Research -- Fractal Analysis in MATLAB: A Tutorial for Neuroscientists -- Methodology to Increase the Computational Speed to Obtain the Fractal Dimension Using GPU Programming -- Fractal Electronics as a Generic Interface to Neurons -- Fractal Geometry meets Computational Intelligence: Future Perspectives
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| 653 |
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|a Neuroscience
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| 653 |
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|a Neurology
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| 653 |
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|a Neurosciences
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| 653 |
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|a Neurology
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
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|a Advances in Neurobiology
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| 028 |
5 |
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|a 10.1007/978-3-031-47606-8
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| 856 |
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|u https://doi.org/10.1007/978-3-031-47606-8?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 612.8
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|a The new edition of the highly popular, The Fractal Geometry of the Brain, reviews the most intriguing applications of fractal analysis in neuroscience with a focus on current and future potential, limits, advantages, and disadvantages. It brings an understanding of fractals to clinicians and researchers even if they do not have a mathematical background, and it serves as a valuable tool for teaching the translational applications of computational fractal-based models to both students and scholars. As a consequence of the novel research developed at Professor Di Ieva's laboratory and other centers around the world, the second edition will explore the use of computational fractal-based analysis in many clinical disciplines and different fields of research, including neurology and neurosurgery, neuroanatomy and psychology, magnetoencephalography (MEG), eye-tracking devices (for the fractal computational characterization of “scanpaths”), deep learning in image analysis, radiomics for the characterization of brain MRIs, characterization of neuropsychological and psychiatric diseases and traits, signal complexity analysis in time series, and functional MRI, amongst others
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